


from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


__snake_case   							=						transforms.Compose(
    [
        transforms.Resize((2_5_6, 2_5_6)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)






def 		_A		(		_lowercase )					-> List[Any]:


     """simple docstring"""


     if isinstance(_lowercase					,  torch.Tensor ):
          return image
     elif isinstance(_lowercase					,  PIL.Image.Image ):
          __UpperCamelCase =			[image]

     __UpperCamelCase =			[trans(img.convert('RGB' ) ) for img in image]
     __UpperCamelCase =			torch.stack(_lowercase )
     return image



class 	__lowerCamelCase			(_a						):







       def __init__(			self: int,A_: Optional[int],A_: Optional[int]					):






            '''simple docstring'''




            super().__init__()

            # make sure scheduler can always be converted to DDIM
            __UpperCamelCase =			DDIMScheduler.from_config(scheduler.config					)

            self.register_modules(unet=A_,scheduler=A_					)







       def 		snake_case_	(			self: Optional[int],A_: Optional[Any]					):






            '''simple docstring'''




            if strength < 0 or strength > 1:
                 raise ValueError(F'''The value of strength should in [0.0, 1.0] but is {strength}'''					)







       def 		snake_case_	(			self: int,A_: Optional[Any],A_: Any,A_: Optional[Any]					):






            '''simple docstring'''




            __UpperCamelCase =			min(int(num_inference_steps * strength					),A_					)

            __UpperCamelCase =			max(num_inference_steps - init_timestep,0					)
            __UpperCamelCase =			self.scheduler.timesteps[t_start:]

            return timesteps, num_inference_steps - t_start







       def 		snake_case_	(			self: Optional[Any],A_: List[str],A_: Optional[Any],A_: Optional[Any],A_: Optional[int],A_: Optional[int],A_: Tuple=None					):






            '''simple docstring'''




            if not isinstance(A_,(torch.Tensor, PIL.Image.Image, list)					):
                 raise ValueError(
                     F'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(A_					)}'''					)

            __UpperCamelCase =			image.to(device=A_,dtype=A_					)

            if isinstance(A_,A_					) and len(A_					) != batch_size:
                 raise ValueError(
                     F'''You have passed a list of generators of length {len(A_					)}, but requested an effective batch'''
                     F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''					)

            __UpperCamelCase =			init_latents.shape
            __UpperCamelCase =			randn_tensor(A_,generator=A_,device=A_,dtype=A_					)

            # get latents
            print('add noise to latents at timestep',A_					)
            __UpperCamelCase =			self.scheduler.add_noise(A_,A_,A_					)
            __UpperCamelCase =			init_latents

            return latents







       @torch.no_grad()
       def __call__(			self: Any,A_: Union[torch.FloatTensor, PIL.Image.Image] = None,A_: float = 0.8,A_: int = 1,A_: Optional[Union[torch.Generator, List[torch.Generator]]] = None,A_: float = 0.0,A_: int = 50,A_: Optional[bool] = None,A_: Optional[str] = "pil",A_: bool = True,):






            '''simple docstring'''




            self.check_inputs(A_					)

            # 2. Preprocess image
            __UpperCamelCase =			preprocess(A_					)

            # 3. set timesteps
            self.scheduler.set_timesteps(A_,device=self.device					)
            __UpperCamelCase,    __UpperCamelCase =			self.get_timesteps(A_,A_,self.device					)
            __UpperCamelCase =			timesteps[:1].repeat(A_					)

            # 4. Prepare latent variables
            __UpperCamelCase =			self.prepare_latents(A_,A_,A_,self.unet.dtype,self.device,A_					)
            __UpperCamelCase =			latents

            # 5. Denoising loop
            for t in self.progress_bar(A_					):
                 # 1. predict noise model_output
                 __UpperCamelCase =			self.unet(A_,A_					).sample

                 # 2. predict previous mean of image x_t-1 and add variance depending on eta
                 # eta corresponds to η in paper and should be between [0, 1]
                 # do x_t -> x_t-1
                 __UpperCamelCase =			self.scheduler.step(
                     A_,A_,A_,eta=A_,use_clipped_model_output=A_,generator=A_,).prev_sample

            __UpperCamelCase =			(image / 2 + 0.5).clamp(0,1					)
            __UpperCamelCase =			image.cpu().permute(0,2,3,1					).numpy()
            if output_type == "pil":
                 __UpperCamelCase =			self.numpy_to_pil(A_					)

            if not return_dict:
                 return (image, latent_timestep.item())

            return ImagePipelineOutput(images=A_					)









'''simple docstring'''






from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


lowerCAmelCase									=   logging.get_logger(__name__)
class 		lowerCamelCase	(   _A							):



	def __init__( self		,   a_		,   a_		,   a_		,   **a_						):
			lowerCAmelCase  :     Tuple       						= feature_size
			lowerCAmelCase  :     int       						= sampling_rate
			lowerCAmelCase  :     str       						= padding_value

			lowerCAmelCase  :     int       						= kwargs.pop("padding_side"		,   "right"						)
			lowerCAmelCase  :     int       						= kwargs.pop("return_attention_mask"		,   a_						)

			super().__init__(**a_						)



	def 	_lowerCamelCase     ( self		,   a_		,   a_ = True		,   a_ = None		,   a_ = False		,   a_ = None		,   a_ = None		,   a_ = None		,   ):
			# If we have a list of dicts, let's convert it in a dict of lists
			# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
			if isinstance(a_		,   (list, tuple)						) and isinstance(processed_features[0]		,   (dict, BatchFeature)						):
					lowerCAmelCase  :     str       						= {
					    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
					}

			# The model's main input name, usually `input_values`, has be passed for padding
			if self.model_input_names[0] not in processed_features:
					raise ValueError(
					    "You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"
					    F''' to this method that includes {self.model_input_names[0]}, but you provided'''
					    F''' {list(processed_features.keys()						)}'''						)

			lowerCAmelCase  :     Any       						= processed_features[self.model_input_names[0]]
			lowerCAmelCase  :     Dict       						= (
			    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
			)

			if len(a_						) == 0:
					if return_attention_mask:
							lowerCAmelCase  :     List[Any]       						= []
					return processed_features

			# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
			# and rebuild them afterwards if no return_tensors is specified
			# Note that we lose the specific device the tensor may be on for PyTorch

			lowerCAmelCase  :     Optional[int]       						= required_input[0]
			if isinstance(a_		,   (list, tuple)						):
					# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
					lowerCAmelCase  :     Optional[Any]       						= 0
					while len(required_input[index]						) == 0:
							index += 1
					if index < len(a_						):
							lowerCAmelCase  :     Union[str, Any]       						= required_input[index][0]

			if return_tensors is None:
					if is_tf_tensor(a_						):
							lowerCAmelCase  :     Tuple       						= "tf"
					elif is_torch_tensor(a_						):
							lowerCAmelCase  :     str       						= "pt"
					elif isinstance(a_		,   (int, float, list, tuple, np.ndarray)						):
							lowerCAmelCase  :     Dict       						= "np"
					else:
							raise ValueError(
							    F'''type of {first_element} unknown: {type(a_						)}. '''
							    "Should be one of a python, numpy, pytorch or tensorflow object."						)

			for key, value in processed_features.items():
					if isinstance(value[0]		,   (int, float)						):
							lowerCAmelCase  :     Dict       						= to_numpy(a_						)
					else:
							lowerCAmelCase  :     Any       						= [to_numpy(a_						) for v in value]

        # Convert padding_strategy in PaddingStrategy
			lowerCAmelCase  :     Optional[int]       						= self._get_padding_strategies(padding=a_		,   max_length=a_						)

			lowerCAmelCase  :     Dict       						= processed_features[self.model_input_names[0]]

			lowerCAmelCase  :     Any       						= len(a_						)
			if not all(len(a_						) == batch_size for v in processed_features.values()						):
					raise ValueError("Some items in the output dictionary have a different batch size than others."						)

			lowerCAmelCase  :     Tuple       						= []
			for i in range(a_						):
					lowerCAmelCase  :     Any       						= {k: v[i] for k, v in processed_features.items()}
					# truncation
					lowerCAmelCase  :     Tuple       						= self._truncate(
					    a_		,   max_length=a_		,   pad_to_multiple_of=a_		,   truncation=a_		,   )
					truncated_inputs.append(a_						)

			if padding_strategy == PaddingStrategy.LONGEST:
					# make sure that `max_length` cannot be longer than the longest truncated length
					lowerCAmelCase  :     Optional[Any]       						= max(len(input_slice[self.model_input_names[0]]						) for input_slice in truncated_inputs						)
					lowerCAmelCase  :     int       						= PaddingStrategy.MAX_LENGTH

			lowerCAmelCase  :     List[Any]       						= {}
			for i in range(a_						):
					# padding
					lowerCAmelCase  :     Optional[int]       						= self._pad(
					    truncated_inputs[i]		,   max_length=a_		,   padding_strategy=a_		,   pad_to_multiple_of=a_		,   return_attention_mask=a_		,   )

					for key, value in outputs.items():
							if key not in batch_outputs:
									lowerCAmelCase  :     Dict       						= []
							if value.dtype is np.dtype(np.floataa						):
									lowerCAmelCase  :     int       						= value.astype(np.floataa						)
							batch_outputs[key].append(a_						)

			return BatchFeature(a_		,   tensor_type=a_						)



	def 	_lowerCamelCase     ( self		,   a_		,   a_ = None		,   a_ = PaddingStrategy.DO_NOT_PAD		,   a_ = None		,   a_ = None		,   ):
			lowerCAmelCase  :     Tuple       						= processed_features[self.model_input_names[0]]

			if padding_strategy == PaddingStrategy.LONGEST:
					lowerCAmelCase  :     Dict       						= len(a_						)

			if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
					lowerCAmelCase  :     int       						= ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

			lowerCAmelCase  :     List[Any]       						= padding_strategy != PaddingStrategy.DO_NOT_PAD and len(a_						) < max_length

			if return_attention_mask and "attention_mask" not in processed_features:
					lowerCAmelCase  :     Any       						= np.ones(len(a_						)		,   dtype=np.intaa						)

			if needs_to_be_padded:
					lowerCAmelCase  :     Dict       						= max_length - len(a_						)
					if self.padding_side == "right":
							if return_attention_mask:
									lowerCAmelCase  :     int       						= np.pad(
									    processed_features["attention_mask"]		,   (0, difference)						)
							lowerCAmelCase  :     Optional[int]       						= ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
							lowerCAmelCase  :     Any       						= np.pad(
							    a_		,   a_		,   "constant"		,   constant_values=self.padding_value						)
					elif self.padding_side == "left":
							if return_attention_mask:
									lowerCAmelCase  :     Dict       						= np.pad(
									    processed_features["attention_mask"]		,   (difference, 0)						)
							lowerCAmelCase  :     Any       						= ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
							lowerCAmelCase  :     Any       						= np.pad(
							    a_		,   a_		,   "constant"		,   constant_values=self.padding_value						)
					else:
							raise ValueError("Invalid padding strategy:" + str(self.padding_side						)						)

			return processed_features



	def 	_lowerCamelCase     ( self		,   a_		,   a_ = None		,   a_ = None		,   a_ = None		,   ):
			if not truncation:
					return processed_features
			elif truncation and max_length is None:
					raise ValueError("When setting ``truncation=True``, make sure that ``max_length`` is defined."						)

			lowerCAmelCase  :     int       						= processed_features[self.model_input_names[0]]

			# find `max_length` that fits `pad_to_multiple_of`
			if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
					lowerCAmelCase  :     Union[str, Any]       						= ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

			lowerCAmelCase  :     Any       						= len(a_						) > max_length

			if needs_to_be_truncated:
					lowerCAmelCase  :     Optional[int]       						= processed_features[self.model_input_names[0]][:max_length]
					if "attention_mask" in processed_features:
							lowerCAmelCase  :     Any       						= processed_features["attention_mask"][:max_length]

			return processed_features





	def 	_lowerCamelCase     ( self		,   a_=False		,   a_=None						):

			# Get padding strategy
			if padding is not False:
					if padding is True:
							lowerCAmelCase  :     str       						= PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
					elif not isinstance(a_		,   a_						):
							lowerCAmelCase  :     str       						= PaddingStrategy(a_						)
					elif isinstance(a_		,   a_						):
							lowerCAmelCase  :     Optional[int]       						= padding
			else:
					lowerCAmelCase  :     Tuple       						= PaddingStrategy.DO_NOT_PAD

			# Set max length if needed
			if max_length is None:
					if padding_strategy == PaddingStrategy.MAX_LENGTH:
							raise ValueError(
							    F'''When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined'''						)

        # Test if we have a padding value
			if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
					raise ValueError(
					    "Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"
					    " as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."						)

			return padding_strategy



